SOTAVerified

Depth Completion

The Depth Completion task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.

Source: LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery , Unsupervised Depth Completion from Visual Inertial Odometry

Papers

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Showing 101–150 of 242 papers

TitleStatusHype
360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network—0
Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion Network for Depth Completion—0
Improving Depth Completion via Depth Feature Upsampling—0
Flexible Depth Completion for Sparse and Varying Point Densities—0
Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency—0
SuperPrimitive: Scene Reconstruction at a Primitive Level—0
MVDD: Multi-View Depth Diffusion Models—0
VioLA: Aligning Videos to 2D LiDAR Scans—0
What You See Is What You Detect: Towards better Object Densification in 3D detectionCode0
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior—0
AugUndo: Scaling Up Augmentations for Monocular Depth Completion and EstimationCode0
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion—0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
Gated Cross-Attention Network for Depth Completion—0
DEUX: Active Exploration for Learning Unsupervised Depth Perception—0
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM—0
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion—0
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion—0
TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo—0
TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation—0
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo—0
Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities—0
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion—0
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results—0
Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion—0
Prior based Sampling for Adaptive LiDARCode0
Enhancing Depth Completion with Multi-View Monitored Distillation—0
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion—0
Learning a Depth Covariance Function—0
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion—0
Aggregating Feature Point Cloud for Depth Completion—0
Sparse SPN: Depth Completion from Sparse Keypoints—0
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion—0
Segmentation-guided Domain Adaptation for Efficient Depth Completion—0
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and ReportCode0
FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth CompletionCode0
M^2-3DLaneNet: Exploring Multi-Modal 3D Lane Detection—0
Spacecraft depth completion based on the gray image and the sparse depth map—0
TransNet: Category-Level Transparent Object Pose Estimation—0
MetaComp: Learning to Adapt for Online Depth Completion—0
SparseFormer: Attention-based Depth Completion Network—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
Deep Depth Completion from Extremely Sparse Data: A Survey—0
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
RGB-Depth Fusion GAN for Indoor Depth Completion—0
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth CompletionCode0
Depth-Independent Depth Completion via Least Square Estimation—0
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All datasetsKITTI Depth CompletionVOIDNYU-Depth V2Matterport3DKITTIKITTI Depth Completion 500 pointsKITTI Depth Completion Eigen SplitPLADVOID-150

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SparseConvsRMSE1,601—Unverified
2NConv-CNNRMSE1,268—Unverified
3VOICEDRMSE1,169.97—Unverified
4ScaffNet-FusionNetRMSE1,121.93—Unverified
5KBNetRMSE1,069.47—Unverified
6Spade-sDRMSE1,035—Unverified
7HMS-NetRMSE937—Unverified
8Spade-RGBsDRMSE918—Unverified
9NConv-CNN-L1RMSE859—Unverified
10NConv-CNN-L2RMSE830—Unverified
#ModelMetricClaimedVerifiedStatus
1SS-S2DMAE178.85—Unverified
2DDPMAE151.86—Unverified
3VOICEDMAE85.05—Unverified
4ScaffNet-FusionNetMAE59.53—Unverified
5KBNetMAE39.8—Unverified
6NLSPNMAE26.74—Unverified
#ModelMetricClaimedVerifiedStatus
1Struct-MDCRMSE0.14—Unverified
2DSNRMSE0.1—Unverified
3NLSPNRMSE0.09—Unverified
#ModelMetricClaimedVerifiedStatus
1SA+SSIM+BCRMSE1.09—Unverified
2DM-LRN-b4RMSE1—Unverified
#ModelMetricClaimedVerifiedStatus
1FusionDepthRMSE1,193.92—Unverified
#ModelMetricClaimedVerifiedStatus
1CFCNetRMSE 2.96—Unverified
#ModelMetricClaimedVerifiedStatus
1DSNREL0.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Struct-MDCMAE1,170.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Struct-MDCMAE111.33—Unverified